Algorithmic composition refers to the use of algorithms (e.g., computer-implemented algorithms) to create music. For example, certain existing music generation algorithms can include one or more computational models.
One of the most difficult challenges posed by algorithmic composition is to successfully incorporate the concept of creativity within the algorithm and the resulting generated music. In particular, creativity is a nebulous concept and algorithm designers typically do not have a clear idea or mechanism of how to express or generate creativity within their music generation algorithms.
As such, one significant disadvantage of most music generation algorithms is that the music that they produce is generally meaningless: since the computers do not have feelings, moods, or intentions, they do not try to describe something with their music in the way that humans do. By contrast, most of human music is referential or descriptive. The reference can be something abstract like an emotion, or something more objective such as a picture or a landscape.
Another major disadvantage associated with algorithmically-generated music is that it typically lacks consistency or structure. Thus, another big challenge of algorithmic composition is creating an algorithm that generates a piece of music that exhibits a globally consistent theme or structure across the entirety of the piece. More particularly, while certain existing techniques may be able to generate snippets of music, such techniques are typically unable to generate an entire piece of music that exhibits a globally consistent theme and/or structure. Thus, a technical challenge exists which can be summarized as the current inability to algorithmically generate music that exhibits a globally consistent theme and/or structure.